README.md CHANGED
@@ -18,240 +18,19 @@ Controlnet's auxiliary models are trained with stable diffusion 1.5. Experimenta
18
  The auxiliary conditioning is passed directly to the diffusers pipeline. If you want to process an image to create the auxiliary conditioning, external dependencies are required.
19
 
20
  Some of the additional conditionings can be extracted from images via additional models. We extracted these
21
- additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/human_pose.git).
22
-
23
- ## Canny edge detection
24
-
25
- Install opencv
26
-
27
- ```sh
28
- $ pip install opencv-contrib-python
29
- ```
30
-
31
- ```python
32
- import cv2
33
- from PIL import Image
34
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
35
- import torch
36
- import numpy as np
37
-
38
- image = Image.open('images/bird.png')
39
- image = np.array(image)
40
-
41
- low_threshold = 100
42
- high_threshold = 200
43
-
44
- image = cv2.Canny(image, low_threshold, high_threshold)
45
- image = image[:, :, None]
46
- image = np.concatenate([image, image, image], axis=2)
47
- image = Image.fromarray(image)
48
-
49
- controlnet = ControlNetModel.from_pretrained(
50
- "fusing/stable-diffusion-v1-5-controlnet-canny",
51
- )
52
-
53
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
54
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
55
- )
56
- pipe.to('cuda')
57
-
58
- image = pipe("bird", image).images[0]
59
-
60
- image.save('images/bird_canny_out.png')
61
- ```
62
-
63
- ![bird](./images/bird.png)
64
-
65
- ![bird_canny](./images/bird_canny.png)
66
-
67
- ![bird_canny_out](./images/bird_canny_out.png)
68
-
69
- ## M-LSD Straight line detection
70
-
71
- Install the additional controlnet models package.
72
-
73
- ```sh
74
- $ pip install git+https://github.com/patrickvonplaten/human_pose.git
75
- ```
76
-
77
- ```py
78
- from PIL import Image
79
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
80
- import torch
81
- from human_pose import MLSDdetector
82
-
83
- mlsd = MLSDdetector.from_pretrained('lllyasviel/ControlNet')
84
-
85
- image = Image.open('images/room.png')
86
-
87
- image = mlsd(image)
88
-
89
- controlnet = ControlNetModel.from_pretrained(
90
- "fusing/stable-diffusion-v1-5-controlnet-mlsd",
91
- )
92
-
93
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
94
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
95
- )
96
- pipe.to('cuda')
97
-
98
- image = pipe("room", image).images[0]
99
-
100
- image.save('images/room_mlsd_out.png')
101
- ```
102
-
103
- ![room](./images/room.png)
104
-
105
- ![room_mlsd](./images/room_mlsd.png)
106
-
107
- ![room_mlsd_out](./images/room_mlsd_out.png)
108
-
109
- ## Pose estimation
110
-
111
- Install the additional controlnet models package.
112
-
113
- ```sh
114
- $ pip install git+https://github.com/patrickvonplaten/human_pose.git
115
- ```
116
-
117
- ```py
118
- from PIL import Image
119
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
120
- import torch
121
- from human_pose import OpenposeDetector
122
-
123
- openpose = OpenposeDetector.from_pretrained('lllyasviel/ControlNet')
124
-
125
- image = Image.open('images/pose.png')
126
-
127
- image = openpose(image)
128
-
129
- controlnet = ControlNetModel.from_pretrained(
130
- "fusing/stable-diffusion-v1-5-controlnet-openpose",
131
- )
132
-
133
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
134
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
135
- )
136
- pipe.to('cuda')
137
-
138
- image = pipe("chef in the kitchen", image).images[0]
139
-
140
- image.save('images/chef_pose_out.png')
141
- ```
142
-
143
- ![pose](./images/pose.png)
144
-
145
- ![openpose](./images/openpose.png)
146
-
147
- ![chef_pose_out](./images/chef_pose_out.png)
148
-
149
- ## Semantic Segmentation
150
-
151
- Semantic segmentation relies on transformers. Transformers is a
152
- dependency of diffusers for running controlnet, so you should
153
- have it installed already.
154
-
155
- ```py
156
- from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
157
- from PIL import Image
158
- import numpy as np
159
- from controlnet_utils import ade_palette
160
- import torch
161
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
162
-
163
- image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
164
- image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
165
-
166
- image = Image.open("./images/house.png").convert('RGB')
167
-
168
- pixel_values = image_processor(image, return_tensors="pt").pixel_values
169
-
170
- with torch.no_grad():
171
- outputs = image_segmentor(pixel_values)
172
-
173
- seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
174
-
175
- color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
176
-
177
- palette = np.array(ade_palette())
178
-
179
- for label, color in enumerate(palette):
180
- color_seg[seg == label, :] = color
181
-
182
- color_seg = color_seg.astype(np.uint8)
183
-
184
- image = Image.fromarray(color_seg)
185
-
186
- controlnet = ControlNetModel.from_pretrained(
187
- "fusing/stable-diffusion-v1-5-controlnet-seg",
188
- )
189
-
190
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
191
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
192
- )
193
- pipe.to('cuda')
194
-
195
- image = pipe("house", image).images[0]
196
-
197
- image.save('./images/house_seg_out.png')
198
- ```
199
-
200
- ![house](images/house.png)
201
-
202
- ![house_seg](images/house_seg.png)
203
-
204
- ![house_seg_out](images/house_seg_out.png)
205
-
206
- ## Depth control
207
-
208
- Depth control relies on transformers. Transformers is a dependency of diffusers for running controlnet, so
209
- you should have it installed already.
210
-
211
- ```py
212
- from transformers import pipeline
213
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
214
- from PIL import Image
215
- import numpy as np
216
-
217
- depth_estimator = pipeline('depth-estimation')
218
-
219
- image = Image.open('./images/stormtrooper.png')
220
- image = depth_estimator(image)['depth']
221
- image = np.array(image)
222
- image = image[:, :, None]
223
- image = np.concatenate([image, image, image], axis=2)
224
- image = Image.fromarray(image)
225
-
226
- controlnet = ControlNetModel.from_pretrained(
227
- "fusing/stable-diffusion-v1-5-controlnet-depth",
228
- )
229
-
230
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
231
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
232
- )
233
- pipe.to('cuda')
234
-
235
- image = pipe("Stormtrooper's lecture", image).images[0]
236
-
237
- image.save('./images/stormtrooper_depth_out.png')
238
- ```
239
-
240
- ![stormtrooper](./images/stormtrooper.png)
241
-
242
- ![stormtrooler_depth](./images/stormtrooper_depth.png)
243
-
244
- ![stormtrooler_depth_out](./images/stormtrooper_depth_out.png)
245
-
246
 
247
  ## Normal map
248
 
 
 
249
  ```py
250
  from PIL import Image
251
  from transformers import pipeline
252
  import numpy as np
253
  import cv2
254
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
 
255
 
256
  image = Image.open("images/toy.png").convert("RGB")
257
 
@@ -281,15 +60,23 @@ image = (image * 127.5 + 127.5).clip(0, 255).astype(np.uint8)
281
  image = Image.fromarray(image)
282
 
283
  controlnet = ControlNetModel.from_pretrained(
284
- "fusing/stable-diffusion-v1-5-controlnet-normal",
285
  )
286
 
287
  pipe = StableDiffusionControlNetPipeline.from_pretrained(
288
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
289
  )
290
- pipe.to('cuda')
291
 
292
- image = pipe("cute toy", image).images[0]
 
 
 
 
 
 
 
 
 
293
 
294
  image.save('images/toy_normal_out.png')
295
  ```
@@ -300,82 +87,10 @@ image.save('images/toy_normal_out.png')
300
 
301
  ![toy_normal_out](./images/toy_normal_out.png)
302
 
303
- ## Scribble
304
-
305
- Install the additional controlnet models package.
306
-
307
- ```sh
308
- $ pip install git+https://github.com/patrickvonplaten/human_pose.git
309
- ```
310
-
311
- ```py
312
- from PIL import Image
313
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
314
- import torch
315
- from human_pose import HEDdetector
316
-
317
- hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
318
-
319
- image = Image.open('images/bag.png')
320
-
321
- image = hed(image, scribble=True)
322
-
323
- controlnet = ControlNetModel.from_pretrained(
324
- "fusing/stable-diffusion-v1-5-controlnet-scribble",
325
- )
326
-
327
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
328
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
329
- )
330
- pipe.to('cuda')
331
-
332
- image = pipe("bag", image).images[0]
333
-
334
- image.save('images/bag_scribble_out.png')
335
- ```
336
-
337
- ![bag](./images/bag.png)
338
-
339
- ![bag_scribble](./images/bag_scribble.png)
340
-
341
- ![bag_scribble_out](./images/bag_scribble_out.png)
342
-
343
- ## HED Boundary
344
-
345
- Install the additional controlnet models package.
346
-
347
- ```sh
348
- $ pip install git+https://github.com/patrickvonplaten/human_pose.git
349
- ```
350
-
351
- ```py
352
- from PIL import Image
353
- from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
354
- import torch
355
- from human_pose import HEDdetector
356
-
357
- hed = HEDdetector.from_pretrained('lllyasviel/ControlNet')
358
-
359
- image = Image.open('images/man.png')
360
-
361
- image = hed(image)
362
-
363
- controlnet = ControlNetModel.from_pretrained(
364
- "fusing/stable-diffusion-v1-5-controlnet-hed",
365
- )
366
-
367
- pipe = StableDiffusionControlNetPipeline.from_pretrained(
368
- "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None
369
- )
370
- pipe.to('cuda')
371
-
372
- image = pipe("oil painting of handsome old man, masterpiece", image).images[0]
373
-
374
- image.save('images/man_hed_out.png')
375
- ```
376
 
377
- ![man](./images/man.png)
378
 
379
- ![man_hed](./images/man_hed.png)
380
 
381
- ![man_hed_out](./images/man_hed_out.png)
 
18
  The auxiliary conditioning is passed directly to the diffusers pipeline. If you want to process an image to create the auxiliary conditioning, external dependencies are required.
19
 
20
  Some of the additional conditionings can be extracted from images via additional models. We extracted these
21
+ additional models from the original controlnet repo into a separate package that can be found on [github](https://github.com/patrickvonplaten/controlnet_aux.git).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
22
 
23
  ## Normal map
24
 
25
+ ### Diffusers
26
+
27
  ```py
28
  from PIL import Image
29
  from transformers import pipeline
30
  import numpy as np
31
  import cv2
32
+ from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
33
+ import torch
34
 
35
  image = Image.open("images/toy.png").convert("RGB")
36
 
 
60
  image = Image.fromarray(image)
61
 
62
  controlnet = ControlNetModel.from_pretrained(
63
+ "fusing/stable-diffusion-v1-5-controlnet-normal", torch_dtype=torch.float16
64
  )
65
 
66
  pipe = StableDiffusionControlNetPipeline.from_pretrained(
67
+ "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
68
  )
 
69
 
70
+ pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
71
+
72
+ # Remove if you do not have xformers installed
73
+ # see https://huggingface.co/docs/diffusers/v0.13.0/en/optimization/xformers#installing-xformers
74
+ # for installation instructions
75
+ pipe.enable_xformers_memory_efficient_attention()
76
+
77
+ pipe.enable_model_cpu_offload()
78
+
79
+ image = pipe("cute toy", image, num_inference_steps=20).images[0]
80
 
81
  image.save('images/toy_normal_out.png')
82
  ```
 
87
 
88
  ![toy_normal_out](./images/toy_normal_out.png)
89
 
90
+ ### Training
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
91
 
92
+ The normal model was trained from an initial model and then a further extended model.
93
 
94
+ The initial normal model was trained on 25,452 normal-image, caption pairs from DIODE. The image captions were generated by BLIP. The model was trained for 100 GPU-hours with Nvidia A100 80G using Stable Diffusion 1.5 as a base model.
95
 
96
+ The extended normal model further trained the initial normal model on "coarse" normal maps. The coarse normal maps were generated using Midas to compute a depth map and then performing normal-from-distance. The model was trained for 200 GPU-hours with Nvidia A100 80G using the initial normal model as a base model.
controlnet_utils.py DELETED
@@ -1,40 +0,0 @@
1
- def ade_palette():
2
- """ADE20K palette that maps each class to RGB values."""
3
- return [[120, 120, 120], [180, 120, 120], [6, 230, 230], [80, 50, 50],
4
- [4, 200, 3], [120, 120, 80], [140, 140, 140], [204, 5, 255],
5
- [230, 230, 230], [4, 250, 7], [224, 5, 255], [235, 255, 7],
6
- [150, 5, 61], [120, 120, 70], [8, 255, 51], [255, 6, 82],
7
- [143, 255, 140], [204, 255, 4], [255, 51, 7], [204, 70, 3],
8
- [0, 102, 200], [61, 230, 250], [255, 6, 51], [11, 102, 255],
9
- [255, 7, 71], [255, 9, 224], [9, 7, 230], [220, 220, 220],
10
- [255, 9, 92], [112, 9, 255], [8, 255, 214], [7, 255, 224],
11
- [255, 184, 6], [10, 255, 71], [255, 41, 10], [7, 255, 255],
12
- [224, 255, 8], [102, 8, 255], [255, 61, 6], [255, 194, 7],
13
- [255, 122, 8], [0, 255, 20], [255, 8, 41], [255, 5, 153],
14
- [6, 51, 255], [235, 12, 255], [160, 150, 20], [0, 163, 255],
15
- [140, 140, 140], [250, 10, 15], [20, 255, 0], [31, 255, 0],
16
- [255, 31, 0], [255, 224, 0], [153, 255, 0], [0, 0, 255],
17
- [255, 71, 0], [0, 235, 255], [0, 173, 255], [31, 0, 255],
18
- [11, 200, 200], [255, 82, 0], [0, 255, 245], [0, 61, 255],
19
- [0, 255, 112], [0, 255, 133], [255, 0, 0], [255, 163, 0],
20
- [255, 102, 0], [194, 255, 0], [0, 143, 255], [51, 255, 0],
21
- [0, 82, 255], [0, 255, 41], [0, 255, 173], [10, 0, 255],
22
- [173, 255, 0], [0, 255, 153], [255, 92, 0], [255, 0, 255],
23
- [255, 0, 245], [255, 0, 102], [255, 173, 0], [255, 0, 20],
24
- [255, 184, 184], [0, 31, 255], [0, 255, 61], [0, 71, 255],
25
- [255, 0, 204], [0, 255, 194], [0, 255, 82], [0, 10, 255],
26
- [0, 112, 255], [51, 0, 255], [0, 194, 255], [0, 122, 255],
27
- [0, 255, 163], [255, 153, 0], [0, 255, 10], [255, 112, 0],
28
- [143, 255, 0], [82, 0, 255], [163, 255, 0], [255, 235, 0],
29
- [8, 184, 170], [133, 0, 255], [0, 255, 92], [184, 0, 255],
30
- [255, 0, 31], [0, 184, 255], [0, 214, 255], [255, 0, 112],
31
- [92, 255, 0], [0, 224, 255], [112, 224, 255], [70, 184, 160],
32
- [163, 0, 255], [153, 0, 255], [71, 255, 0], [255, 0, 163],
33
- [255, 204, 0], [255, 0, 143], [0, 255, 235], [133, 255, 0],
34
- [255, 0, 235], [245, 0, 255], [255, 0, 122], [255, 245, 0],
35
- [10, 190, 212], [214, 255, 0], [0, 204, 255], [20, 0, 255],
36
- [255, 255, 0], [0, 153, 255], [0, 41, 255], [0, 255, 204],
37
- [41, 0, 255], [41, 255, 0], [173, 0, 255], [0, 245, 255],
38
- [71, 0, 255], [122, 0, 255], [0, 255, 184], [0, 92, 255],
39
- [184, 255, 0], [0, 133, 255], [255, 214, 0], [25, 194, 194],
40
- [102, 255, 0], [92, 0, 255]]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
images/bag.png DELETED
Binary file (462 kB)
 
images/bag_scribble.png DELETED
Binary file (11 kB)
 
images/bag_scribble_out.png DELETED
Binary file (556 kB)
 
images/bird.png DELETED

Git LFS Details

  • SHA256: cad49fc7d3071b2bcd078bc8dde365f8fa62eaa6d43705fd50c212794a3aac35
  • Pointer size: 132 Bytes
  • Size of remote file: 1.07 MB
images/bird_canny.png DELETED
Binary file (29.1 kB)
 
images/bird_canny_out.png DELETED
Binary file (845 kB)
 
images/chef_pose_out.png DELETED
Binary file (570 kB)
 
images/house.png DELETED
Binary file (391 kB)
 
images/house_seg.png DELETED
Binary file (3.68 kB)
 
images/house_seg_out.png DELETED
Binary file (472 kB)
 
images/man.png DELETED
Binary file (773 kB)
 
images/man_hed.png DELETED
Binary file (118 kB)
 
images/man_hed_out.png DELETED
Binary file (737 kB)
 
images/openpose.png DELETED
Binary file (6.55 kB)
 
images/pose.png DELETED
Binary file (592 kB)
 
images/room.png DELETED
Binary file (637 kB)
 
images/room_mlsd.png DELETED
Binary file (9.06 kB)
 
images/room_mlsd_out.png DELETED
Binary file (575 kB)
 
images/stormtrooper.png DELETED
Binary file (218 kB)
 
images/stormtrooper_depth.png DELETED
Binary file (54.1 kB)
 
images/stormtrooper_depth_out.png DELETED
Binary file (343 kB)
 
images/toy_normal_out.png CHANGED